Did Patients With COVID-19 Receive Timely Treatment in the Early Epidemic? A Systematic Review and Meta-analysis (preprint)

Background: COVID-19 showed a significant difference in case fatality rate between different regions at the early stage of the epidemic. In addition to the well-known factors such as age structure, detection efficiency, and race, there was also a possibility that medical resource shortage caused the increase of the case fatality rate in some regions. Methods: Medline, Cochrane Library, Embase, Web of Science, CBM, CNKI, and Wan fang of identified articles were searched through 29 June 2020. Cohort studies and case series with duration information on COVID-19 patients were included. Two independent reviewers extracted the data using a standardized data collection form and assessed the risk of bias. Data were synthesized through description and analysis methods including a meta-analysis.Results: A total of 109 articles were retrieved. The time interval from onset to the first medical visit of COVID-19 patients in China was 3.38±1.55 days (corresponding intervals in Hubei province, non-Hubei provinces, Wuhan, Hubei provinces without Wuhan were 4.22±1.13 days, 3.10±1.57 days, 4.20±0.97 days, and 4.34±1.72 days, respectively). The time interval from onset to the hospitalization of COVID-19 patients in China was 8.35±6.83 days (same corresponding intervals were 12.94±7.43 days, 4.17±1.45 days, 14.86±7.12 days, and 5.36±1.19 days, respectively), and when it was outside China, this interval was 5.27±1.19 days. Conclusion: In the early stage of the COVID-19 epidemic, patients with COVID-19 did not receive timely treatment, resulting in a higher case fatality rate in Hubei province, partly due to the relatively insufficient and unequal medical resources. This research suggested that additional deaths caused by the out-of-control epidemic can be avoided if prevention and control work is carried out at the early stage of the epidemic.PROSPERO registration number CRD42020195606.

Voxel-level forecast system for lesion development in patients with COVID-19 (preprint)

The global spread of COVID-19 seriously endangers human health and even lives. By predicting patients' individualized disease development and further performing intervention in time, we may rationalize scarce medical resources and reduce mortality. Based on 1337 multi- stage ([≥]3) high-resolution chest computed tomography (CT) images of 417 infected patients from three centers in the epidemic area, we proposed a random forest + cellular automata (RF+CA) model to forecast voxel-level lesion development of patients with COVID-19. The model showed a promising prediction performance (Dice similarity coefficient [DSC] = 71.1%, Kappa coefficient = 0.612, Figure of Merit [FoM] = 0.257, positional accuracy [PA] = 3.63) on the multicenter dataset. Using this model, multiple driving factors for the development of lesions were determined, such as distance to various interstitials in the lung, distance to the pleura, etc. The driving processes of these driving factors were further dissected and explained in depth from the perspective of pathophysiology, to explore the mechanism of individualized development of COVID-19 disease. The complete codes of the forecast system are available at https://github.com/keyunj/VVForecast_covid19.

Development and Evaluation of an AI System for COVID-19 (preprint)

Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. We proposed an artificial intelligence (AI) system for fast COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.17%, a sensitivity of 90.19%, and a specificity of 95.76% for COVID-19 on internal test cohort of 3,203 scans and AUC of 97.77% on the publicly available CC-CCII database with 1,943 test samples. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared. Detailed interpretation of deep network is also performed to relate AI results with CT findings. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.